Recent Posts

A Long/Short scoring engine is at the core of our model. I
would love to call it proprietary, but it’s scoring a number of
readily available ratios from free data and from that generating
a unique score. Is this combination of data points unique? Maybe.
But I doubt it.

While the model is replicable and other shops
likely use similar ones, ours is focused on extremely liquid
strategy. I don’t mind discussing the nuts and bolts of how it
operates in the wild.

This was built around the Excel file made available by InvestExcel, a copy of the original file before I
modified it is here.

Below you will find a snap shot of the Stock Comparison Scoring
Engine. Using 40 different items to compare and contrast two like
stocks, it generates a unique relative score of 1 or 0 per line
compared. These are then combined to give us the relative score
between any two stocks compared against each other.

Below is the score for Exxon & Chevron, as compared against
each other using yesterday’s data.

The relative score will change in value as new
information is available. The idea is to run the model daily and
build a database of the rolling scores for each paired trade of
interest.

The model will track the trend in the pairs, giving us insight
into historical divergent and expected mean reversion
estimates. In addition, it generates the best timing of the
pairs themselves.

While Alfred Jones, who founded the first hedge fund, had his
organization track a form of volatility and weight their pairs
based on a volatility-rated value, we are going to take a
different approach. We will use equal dollar weighting on same
sub-sector pairs.

I prefer to use what I think of as natural pairs. If they belong
to the same business sector, and preferably sub-sectors, that
makes a lot more sense than using cross-sector pairs. I don’t
believe in the lowering of risk, just because historically if the
volatility or rate of change between two stocks has been X, it
will stay X.

The model uses a fundamental bias structure, so it looks to
capture the change between two competitors. We are only
focused on the rate of change between two nearly identical
stocks. This strategy is designed to be as uncorrelated with the
index as possible.

While a relative paired score is nice to have, a baseline is
needed so I’m working on adding that to the model. Below is a
snap shot of the Sub-Sector Comparison Engine.

The next step will be to recode this to generate a
sub-sector score for all stocks.

This helps us identify the best natural pairs in a sector. I will
post on the Sub-Sector Comparison Engine, once I have it polished
and generating sector-wide scores.

The model being discussed in these blog posts is an example model
only. Clients of JHB Capital will see a new, and
completely different set of paired trades in their own account’s
once the model is in operation.

This is an example of a copy of the model only. This model has
three intentionally constructed trades out of nine pairs that are
designed to test the models outputs.

As such, do not invest in this model or its results blindly. You
should consult with your adviser before investing in
anything you find on the internet for free. Everything in this
post is for example only.

If you have any comments or thoughts about how I can improve on
this design please join me in the comments section of this or the
first article in the series.